I recently heard the term “analytics translator” for the first time, from my colleague Henry Zeringue. He’s a former academic who has built up a long history in industry wearing a number of data-scientific and analytics hats; he now feels that analytics translator is the really the role that he plays these days in his organization (a large integrated healthcare delivery system).
As laid out by McKinsey, the analytics translator combines skills such as
- domain knowledge
- general technical fluency
- project-management skills
- entrepreneurial spririt
to serve as something of a bridge between business leadership on the one hand and analytics resources on the other. McKinsey also contrasts the analytics translator with other roles by noting that they “are neither data architects nor data engineers. They’re not even necessarily dedicated analytics professionals, and they don’t possess deep technical expertise in programming or modeling.” I’d instead phrase that by saying that they don’t necessarily possess deep technical expertise, but often (usually?) will, as in the case of someone like Henry who has come to the role by way of extended stints as a technical expert.
Upon reflection, I realized that the term neatly captures a lot of what I try to do, on a small scale, at innovu, a software company that delivers healthcare analytics and human capital risk management through a web-based platform. We have only about 75 employees, yet a lot of varied backgrounds: data scientists and engineers, web developers, health actuaries, benefits consultants, salespeople, etc., all of whom need to be on the same page as we determine e.g.
- what do our customers need?
- what do we need to build to satisfy those needs?
- how do we build it?
- how do we communicate what will be delivered, and explain it once it is?
I was hire #35 or so two-plus years ago, so between turnover and the addition of many new hires, I have around here what counts for “the most important quality of a successful translator: deep company knowledge” (citing McKinsey again). And one of the draws to this particular startup for me was literally the fact that I thought my diverse background would allow me to speak the several “languages” that are spoken in different parts of the company, and serve as a useful bridge as we answer the questions above.
While I don’t think I’d put Analytics Translator on my business card, I do think serving that role is a primary way that I make myself useful, in addition to more straight-up analytical and actuarial expertise. I’ll be curious to see if the role grows in terms of its formal presence in the org charts of analytics firms.